Using Regression Analysis for Automated Material Selection in Smart Manufacturing

Ivan Pavlenko, Ján Piteľ, Vitalii Ivanov, Kristina Berladir, Jana Mižáková, Vitalii Kolos and Justyna Trojanowska
Additional contact information
Ivan Pavlenko: Department of Computational Mechanics Named after Volodymyr Martsynkovskyy, Sumy State University, 40007 Sumy, Ukraine
Ján Piteľ: Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia
Vitalii Ivanov: Department of Manufacturing Engineering, Machines and Tools, Sumy State University, 40007 Sumy, Ukraine
Kristina Berladir: Department of Applied Materials Science and Technology of Constructional Materials, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine
Jana Mižáková: Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia
Vitalii Kolos: Department of Manufacturing Engineering, Machines and Tools, Sumy State University, 40007 Sumy, Ukraine
Justyna Trojanowska: Department of Production Engineering, Poznan University of Technology, 5, M. Sklodowskej-Curie Sq., 60-965 Poznan, Poland

Mathematics, 2022, vol. 10, issue 11, 1-16

Abstract: In intelligent manufacturing, the phase content and physical and mechanical properties of construction materials can vary due to different suppliers of blanks manufacturers. Therefore, evaluating the composition and properties for implementing a decision-making approach in material selection using up-to-date software is a topical problem in smart manufacturing. Therefore, the article aims to develop a comprehensive automated material selection approach. The proposed method is based on the comprehensive use of normalization and probability approaches and the linear regression procedure formulated in a matrix form. As a result of the study, analytical dependencies for automated material selection were developed. Based on the hypotheses about the impact of the phase composition on physical and mechanical properties, the proposed approach was proven qualitatively and quantitively for carbon steels from AISI 1010 to AISI 1060. The achieved results allowed evaluating the phase composition and physical properties for an arbitrary material from a particular group by its mechanical properties. Overall, an automated material selection approach based on decision-making criteria is helpful for mechanical engineering, smart manufacturing, and industrial engineering purposes.

Keywords: mechanical properties; phase composition; process innovation; predictive maintenance; decision-making approach; industrial growth (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/11/1888/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/11/1888/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:11:p:1888-:d:829344

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().